Connected vehicle penetration rate for estimation of arterial measures of effectiveness

The Connected Vehicle (CV) technology is a mobile platform that enables a new dimension of data exchange among vehicles and between vehicles and infrastructure. This data source could improve the estimation of Measures of Effectiveness (MOEs) for traffic operations in real-time, allowing to perfectly monitor traffic states after being fully adopted. However, as with any novel technology, the CV adoption will be a gradual process. This research focuses on determining minimum CV technology penetration rates that would guarantee accurate MOE estimates on signalized arterials. First, the authors present estimation methods for various MOEs such as average speed, number of stops, acceleration noise, and delay, followed by an initial assessment of the penetration rates required to accurately estimate them in undersaturated and oversaturated conditions. Next, the authors propose a methodology to determine the minimum CV market penetration rates to guarantee accurate MOE estimates as a function of traffic conditions, signal settings, sampling duration, and the MOE variability. A correction factor is also provided to account for small vehicle populations where sampling is done without replacement. The methodology is tested in a simulated segment of the San Pablo Avenue arterial in Berkeley, CA. The outcomes show that the minimum penetration rate required can be estimated within 1% for most MOEs under a wide range of traffic conditions. The proposed methodology can be used to determine if MOE estimates obtained with a portion of CV equipped vehicles can yield accurate enough results. The methodology could also be used to develop and assess control strategies towards improved arterial traffic operations.

• Record URL:
  https://doi.org/10.1016/j.trc.2015.08.013
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X15003149
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Argote-Cabañero, Juan
  • Christofa, Eleni
  • Skabardonis, Alexander
• Publication Date: 2015-11

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 298-312
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 60
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Arterial highways; Market share; Measures of effectiveness; Mobile communication systems; Traffic estimation
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Vehicles and Equipment; I72: Traffic and Transport Planning; I90: Vehicles;

Filing Info

• Accession Number: 01581813
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 25 2015 9:14AM